Weed Portfolio

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import math

import warnings
warnings.filterwarnings("ignore")

# yahoo finance data 
import yfinance as yf
yf.pdr_override()

# input
title = "Weed"
symbols = ['ABBV', 'BUD', 'MO','TAP','SMG','CGC', 'GWPH', 'CRON', 'ACB', 'TLRY', 'CRBP', 'CTST', 'NBEV', 'TRTC', 'CANN', 'MJ']
start = '2018-01-01'
end = '2020-06-25'

df = pd.DataFrame()
for s in symbols:
    df[s] = yf.download(s,start,end)['Adj Close']

[*********************100%***********************]  1 of 1 completed
[*********************100%***********************]  1 of 1 completed
[*********************100%***********************]  1 of 1 completed
[*********************100%***********************]  1 of 1 completed
[*********************100%***********************]  1 of 1 completed
[*********************100%***********************]  1 of 1 completed
[*********************100%***********************]  1 of 1 completed
[*********************100%***********************]  1 of 1 completed
[*********************100%***********************]  1 of 1 completed
[*********************100%***********************]  1 of 1 completed
[*********************100%***********************]  1 of 1 completed
[*********************100%***********************]  1 of 1 completed
[*********************100%***********************]  1 of 1 completed
[*********************100%***********************]  1 of 1 completed
[*********************100%***********************]  1 of 1 completed
[*********************100%***********************]  1 of 1 completed

from datetime import datetime
from dateutil import relativedelta

d1 = datetime.strptime(start, "%Y-%m-%d")
d2 = datetime.strptime(end, "%Y-%m-%d")
delta = relativedelta.relativedelta(d2,d1)
print('How many years of investing?')
print('%s years' % delta.years)

How many years of investing?
2 years

number_of_years = delta.years

days = (df.index[-1] - df.index[0]).days
days

904

df.head()

	ABBV	BUD	MO	TAP	SMG	CGC	GWPH	CRON	ACB	TLRY	CRBP	CTST	NBEV	TRTC	CANN	MJ
Date
2018-01-02	85.888794	104.851608	59.916634	76.489159	102.167892	25.879999	134.089996	NaN	113.760002	NaN	7.95	NaN	2.220	6.90	8.15	32.423748
2018-01-03	87.232849	105.663048	59.696426	75.754410	102.346863	28.650000	134.410004	NaN	135.720001	NaN	8.50	NaN	2.177	6.15	10.35	34.465618
2018-01-04	86.735374	105.952171	59.459267	76.265945	100.001511	25.889999	133.919998	NaN	125.400002	NaN	8.35	NaN	2.120	4.20	7.76	32.332596
2018-01-05	88.245255	107.015411	59.628662	77.130905	101.395546	27.389999	133.429993	NaN	127.320000	NaN	7.80	NaN	2.170	5.10	8.06	33.225918
2018-01-08	86.831360	106.278618	59.552422	77.958679	102.770699	32.110001	131.500000	NaN	134.220001	NaN	8.10	NaN	2.140	5.55	9.87	35.021660

df.tail()

	ABBV	BUD	MO	TAP	SMG	CGC	GWPH	CRON	ACB	TLRY	CRBP	CTST	NBEV	TRTC	CANN	MJ
Date
2020-06-18	96.230003	51.779999	41.470001	39.080002	129.850006	17.180000	131.179993	6.56	13.01	8.56	7.97	NaN	1.74	0.13	0.4725	13.66
2020-06-19	96.709999	50.439999	40.689999	38.029999	129.429993	17.330000	129.710007	6.42	13.37	8.30	7.68	NaN	1.70	0.12	0.4600	13.51
2020-06-22	97.269997	50.880001	40.049999	37.950001	130.149994	17.370001	125.849998	6.45	13.72	8.41	7.84	NaN	1.64	0.11	0.4101	13.50
2020-06-23	97.309998	52.099998	40.340000	37.970001	131.929993	17.240000	125.000000	6.53	13.58	8.56	8.15	NaN	1.62	0.11	0.4280	13.56
2020-06-24	95.139999	48.919998	39.430000	36.500000	129.580002	16.709999	121.680000	6.38	13.67	8.15	7.96	NaN	1.50	0.11	0.4111	13.17

plt.figure(figsize=(12,8))
plt.plot(df)
plt.title(title + ' Closing Price')
plt.legend(labels=df.columns)

<matplotlib.legend.Legend at 0x20bd7a254e0>

# Normalize the data
normalize = (df - df.min())/ (df.max() - df.min())

plt.figure(figsize=(18,12))
plt.plot(normalize)
plt.title(title + ' Stocks Normalize')
plt.legend(labels=normalize.columns)

<matplotlib.legend.Legend at 0x20bdadcf0b8>

stock_rets = df.pct_change().dropna()

plt.figure(figsize=(12,8))
plt.plot(stock_rets)
plt.title(title + ' Stocks Returns')
plt.legend(labels=stock_rets.columns)

<matplotlib.legend.Legend at 0x20bdabd9828>

plt.figure(figsize=(12,8))
plt.plot(stock_rets.cumsum())
plt.title(title + ' Stocks Returns Cumulative Sum')
plt.legend(labels=stock_rets.columns)

<matplotlib.legend.Legend at 0x20bdac5d908>

sns.set(style='ticks')
ax = sns.pairplot(stock_rets, diag_kind='hist')

nplot = len(stock_rets.columns)
for i in range(nplot) :
    for j in range(nplot) :
        ax.axes[i, j].locator_params(axis='x', nbins=6, tight=True)

ax = sns.PairGrid(stock_rets)
ax.map_upper(plt.scatter, color='purple')
ax.map_lower(sns.kdeplot, color='blue')
ax.map_diag(plt.hist, bins=30)
for i in range(nplot) :
    for j in range(nplot) :
        ax.axes[i, j].locator_params(axis='x', nbins=6, tight=True)

plt.figure(figsize=(10,10))
corr = stock_rets.corr()

# plot the heatmap
sns.heatmap(corr, 
        xticklabels=corr.columns,
        yticklabels=corr.columns,
            cmap="Reds")

<matplotlib.axes._subplots.AxesSubplot at 0x20be2418ac8>

# Box plot
stock_rets.plot(kind='box',figsize=(24,8))

<matplotlib.axes._subplots.AxesSubplot at 0x20be3454d30>

rets = stock_rets.dropna()

plt.figure(figsize=(16,8))
plt.scatter(rets.std(), rets.mean(),alpha = 0.5)

plt.title('Stocks Risk & Returns')
plt.xlabel('Risk')
plt.ylabel('Expected Returns')
plt.grid(which='major')

for label, x, y in zip(rets.columns, rets.std(), rets.mean()):
    plt.annotate(
        label, 
        xy = (x, y), xytext = (50, 50),
        textcoords = 'offset points', ha = 'right', va = 'bottom',
        arrowprops = dict(arrowstyle = '-', connectionstyle = 'arc3,rad=-0.3'))

rets = stock_rets.dropna()
area = np.pi*20.0

sns.set(style='darkgrid')
plt.figure(figsize=(16,8))
plt.scatter(rets.std(), rets.mean(), s=area)
plt.xlabel("Risk", fontsize=15)
plt.ylabel("Expected Return", fontsize=15)
plt.title("Return vs. Risk for Stocks", fontsize=20)

for label, x, y in zip(rets.columns, rets.std(), rets.mean()) : 
    plt.annotate(label, xy=(x,y), xytext=(50, 0), textcoords='offset points',
                arrowprops=dict(arrowstyle='-', connectionstyle='bar,angle=180,fraction=-0.2'),
                bbox=dict(boxstyle="round", fc="w"))

rest_rets = rets.corr()
pair_value = rest_rets.abs().unstack()
pair_value.sort_values(ascending = False)

MJ    MJ      1.000000
CANN  CANN    1.000000
BUD   BUD     1.000000
MO    MO      1.000000
TAP   TAP     1.000000
SMG   SMG     1.000000
CGC   CGC     1.000000
GWPH  GWPH    1.000000
CRON  CRON    1.000000
ACB   ACB     1.000000
TLRY  TLRY    1.000000
CRBP  CRBP    1.000000
CTST  CTST    1.000000
NBEV  NBEV    1.000000
TRTC  TRTC    1.000000
ABBV  ABBV    1.000000
MJ    CGC     0.810819
CGC   MJ      0.810819
MJ    CRON    0.782384
CRON  MJ      0.782384
ACB   MJ      0.725535
MJ    ACB     0.725535
CRON  CGC     0.653592
CGC   CRON    0.653592
TLRY  MJ      0.640486
MJ    TLRY    0.640486
ACB   CGC     0.613705
CGC   ACB     0.613705
MJ    CTST    0.536440
CTST  MJ      0.536440
                ...   
CRON  ABBV    0.151795
ABBV  CRON    0.151795
BUD   CANN    0.148070
CANN  BUD     0.148070
CGC   ABBV    0.142989
ABBV  CGC     0.142989
CTST  TAP     0.140202
TAP   CTST    0.140202
ACB   ABBV    0.132702
ABBV  ACB     0.132702
MO    TLRY    0.130087
TLRY  MO      0.130087
CTST  TRTC    0.126612
TRTC  CTST    0.126612
MO    CTST    0.124939
CTST  MO      0.124939
NBEV  CANN    0.122163
CANN  NBEV    0.122163
ABBV  CANN    0.111023
CANN  ABBV    0.111023
      TAP     0.107233
TAP   CANN    0.107233
CTST  ABBV    0.106502
ABBV  CTST    0.106502
TRTC  TLRY    0.093788
TLRY  TRTC    0.093788
TAP   TLRY    0.081623
TLRY  TAP     0.081623
MO    CANN    0.063279
CANN  MO      0.063279
Length: 256, dtype: float64

# Normalized Returns Data
Normalized_Value = ((rets[:] - rets[:].min()) /(rets[:].max() - rets[:].min()))
Normalized_Value.head()

	ABBV	BUD	MO	TAP	SMG	CGC	GWPH	CRON	ACB	TLRY	CRBP	CTST	NBEV	TRTC	CANN	MJ
Date
2018-07-20	0.604578	0.635554	0.561502	0.435226	0.551081	0.310425	0.480990	0.482049	0.184212	0.732799	0.203233	0.363230	0.435448	0.411433	0.397675	0.524747
2018-07-23	0.672953	0.605948	0.495865	0.535980	0.549957	0.506239	0.482726	0.542339	0.256214	0.350436	0.202740	0.468312	0.316607	0.411062	0.395720	0.617023
2018-07-24	0.746747	0.599176	0.534830	0.591846	0.505120	0.362205	0.486289	0.508074	0.133584	0.206493	0.215911	0.391442	0.256177	0.446355	0.377724	0.470588
2018-07-25	0.720006	0.658794	0.488151	0.578326	0.570751	0.434735	0.522931	0.573099	0.194646	0.412568	0.215780	0.355515	0.329082	0.457614	0.491436	0.526045
2018-07-26	0.686147	0.409254	0.349327	0.500146	0.579742	0.355298	0.553044	0.519172	0.202836	0.358694	0.215646	0.317725	0.314885	0.493130	0.398628	0.461898

Normalized_Value.corr()

	ABBV	BUD	MO	TAP	SMG	CGC	GWPH	CRON	ACB	TLRY	CRBP	CTST	NBEV	TRTC	CANN	MJ
ABBV	1.000000	0.299752	0.345455	0.341370	0.376258	0.142989	0.303617	0.151795	0.132702	0.158178	0.276853	0.106502	0.175593	0.171783	0.111023	0.290858
BUD	0.299752	1.000000	0.341752	0.487035	0.300006	0.264805	0.290838	0.159286	0.224127	0.224583	0.238561	0.225768	0.212874	0.228349	0.148070	0.379891
MO	0.345455	0.341752	1.000000	0.480350	0.369055	0.203886	0.251809	0.233539	0.187347	0.130087	0.197093	0.124939	0.215947	0.216387	0.063279	0.350375
TAP	0.341370	0.487035	0.480350	1.000000	0.394010	0.204026	0.282370	0.235371	0.213307	0.081623	0.219056	0.140202	0.220178	0.155940	0.107233	0.349039
SMG	0.376258	0.300006	0.369055	0.394010	1.000000	0.254896	0.310430	0.294889	0.274614	0.253283	0.311451	0.194524	0.217895	0.210210	0.185494	0.458283
CGC	0.142989	0.264805	0.203886	0.204026	0.254896	1.000000	0.283343	0.653592	0.613705	0.489689	0.270534	0.459309	0.283808	0.251091	0.352733	0.810819
GWPH	0.303617	0.290838	0.251809	0.282370	0.310430	0.283343	1.000000	0.279702	0.211599	0.176141	0.413548	0.188948	0.271676	0.306750	0.195904	0.449297
CRON	0.151795	0.159286	0.233539	0.235371	0.294889	0.653592	0.279702	1.000000	0.535275	0.488517	0.312369	0.317387	0.332213	0.206619	0.245535	0.782384
ACB	0.132702	0.224127	0.187347	0.213307	0.274614	0.613705	0.211599	0.535275	1.000000	0.416411	0.282352	0.343071	0.264705	0.204751	0.258721	0.725535
TLRY	0.158178	0.224583	0.130087	0.081623	0.253283	0.489689	0.176141	0.488517	0.416411	1.000000	0.166846	0.298170	0.373232	0.093788	0.258778	0.640486
CRBP	0.276853	0.238561	0.197093	0.219056	0.311451	0.270534	0.413548	0.312369	0.282352	0.166846	1.000000	0.254839	0.360862	0.211844	0.212167	0.447811
CTST	0.106502	0.225768	0.124939	0.140202	0.194524	0.459309	0.188948	0.317387	0.343071	0.298170	0.254839	1.000000	0.235262	0.126612	0.271966	0.536440
NBEV	0.175593	0.212874	0.215947	0.220178	0.217895	0.283808	0.271676	0.332213	0.264705	0.373232	0.360862	0.235262	1.000000	0.230254	0.122163	0.444827
TRTC	0.171783	0.228349	0.216387	0.155940	0.210210	0.251091	0.306750	0.206619	0.204751	0.093788	0.211844	0.126612	0.230254	1.000000	0.179612	0.315233
CANN	0.111023	0.148070	0.063279	0.107233	0.185494	0.352733	0.195904	0.245535	0.258721	0.258778	0.212167	0.271966	0.122163	0.179612	1.000000	0.368335
MJ	0.290858	0.379891	0.350375	0.349039	0.458283	0.810819	0.449297	0.782384	0.725535	0.640486	0.447811	0.536440	0.444827	0.315233	0.368335	1.000000

normalized_rets = Normalized_Value.corr()
normalized_pair_value = normalized_rets.abs().unstack()
normalized_pair_value.sort_values(ascending = False)

MJ    MJ      1.000000
CANN  CANN    1.000000
BUD   BUD     1.000000
MO    MO      1.000000
TAP   TAP     1.000000
SMG   SMG     1.000000
CGC   CGC     1.000000
GWPH  GWPH    1.000000
CRON  CRON    1.000000
ACB   ACB     1.000000
TLRY  TLRY    1.000000
CRBP  CRBP    1.000000
CTST  CTST    1.000000
NBEV  NBEV    1.000000
TRTC  TRTC    1.000000
ABBV  ABBV    1.000000
MJ    CGC     0.810819
CGC   MJ      0.810819
MJ    CRON    0.782384
CRON  MJ      0.782384
ACB   MJ      0.725535
MJ    ACB     0.725535
CRON  CGC     0.653592
CGC   CRON    0.653592
TLRY  MJ      0.640486
MJ    TLRY    0.640486
ACB   CGC     0.613705
CGC   ACB     0.613705
MJ    CTST    0.536440
CTST  MJ      0.536440
                ...   
CRON  ABBV    0.151795
ABBV  CRON    0.151795
BUD   CANN    0.148070
CANN  BUD     0.148070
CGC   ABBV    0.142989
ABBV  CGC     0.142989
CTST  TAP     0.140202
TAP   CTST    0.140202
ACB   ABBV    0.132702
ABBV  ACB     0.132702
MO    TLRY    0.130087
TLRY  MO      0.130087
CTST  TRTC    0.126612
TRTC  CTST    0.126612
MO    CTST    0.124939
CTST  MO      0.124939
NBEV  CANN    0.122163
CANN  NBEV    0.122163
ABBV  CANN    0.111023
CANN  ABBV    0.111023
      TAP     0.107233
TAP   CANN    0.107233
CTST  ABBV    0.106502
ABBV  CTST    0.106502
TRTC  TLRY    0.093788
TLRY  TRTC    0.093788
TAP   TLRY    0.081623
TLRY  TAP     0.081623
MO    CANN    0.063279
CANN  MO      0.063279
Length: 256, dtype: float64

print("Stock returns: ")
print(rets.mean())
print('-' * 50)
print("Stock risks:")
print(rets.std())

Stock returns: 
ABBV    0.000553
BUD    -0.001157
MO     -0.000263
TAP    -0.000762
SMG     0.001381
CGC     0.000400
GWPH    0.000103
CRON    0.001340
ACB    -0.001118
TLRY    0.001221
CRBP    0.001782
CTST   -0.002506
NBEV    0.002995
TRTC   -0.002544
CANN   -0.002204
MJ     -0.000878
dtype: float64
--------------------------------------------------
Stock risks:
ABBV    0.020498
BUD     0.023911
MO      0.019894
TAP     0.022924
SMG     0.025713
CGC     0.050582
GWPH    0.032086
CRON    0.054711
ACB     0.070286
TLRY    0.083543
CRBP    0.046494
CTST    0.061369
NBEV    0.090104
TRTC    0.081239
CANN    0.066023
MJ      0.029951
dtype: float64

table = pd.DataFrame()
table['Returns'] = rets.mean()
table['Risk'] = rets.std()
table.sort_values(by='Returns')

	Returns	Risk
TRTC	-0.002544	0.081239
CTST	-0.002506	0.061369
CANN	-0.002204	0.066023
BUD	-0.001157	0.023911
ACB	-0.001118	0.070286
MJ	-0.000878	0.029951
TAP	-0.000762	0.022924
MO	-0.000263	0.019894
GWPH	0.000103	0.032086
CGC	0.000400	0.050582
ABBV	0.000553	0.020498
TLRY	0.001221	0.083543
CRON	0.001340	0.054711
SMG	0.001381	0.025713
CRBP	0.001782	0.046494
NBEV	0.002995	0.090104

table.sort_values(by='Risk')

	Returns	Risk
MO	-0.000263	0.019894
ABBV	0.000553	0.020498
TAP	-0.000762	0.022924
BUD	-0.001157	0.023911
SMG	0.001381	0.025713
MJ	-0.000878	0.029951
GWPH	0.000103	0.032086
CRBP	0.001782	0.046494
CGC	0.000400	0.050582
CRON	0.001340	0.054711
CTST	-0.002506	0.061369
CANN	-0.002204	0.066023
ACB	-0.001118	0.070286
TRTC	-0.002544	0.081239
TLRY	0.001221	0.083543
NBEV	0.002995	0.090104

rf = 0.01
table['Sharpe Ratio'] = (table['Returns'] - rf) / table['Risk']
table

	Returns	Risk	Sharpe Ratio
ABBV	0.000553	0.020498	-0.460876
BUD	-0.001157	0.023911	-0.466629
MO	-0.000263	0.019894	-0.515883
TAP	-0.000762	0.022924	-0.469473
SMG	0.001381	0.025713	-0.335198
CGC	0.000400	0.050582	-0.189792
GWPH	0.000103	0.032086	-0.308445
CRON	0.001340	0.054711	-0.158285
ACB	-0.001118	0.070286	-0.158187
TLRY	0.001221	0.083543	-0.105087
CRBP	0.001782	0.046494	-0.176764
CTST	-0.002506	0.061369	-0.203778
NBEV	0.002995	0.090104	-0.077747
TRTC	-0.002544	0.081239	-0.154405
CANN	-0.002204	0.066023	-0.184838
MJ	-0.000878	0.029951	-0.363179

table['Max Returns'] = rets.max()

table['Min Returns'] = rets.min()

table['Median Returns'] = rets.median()

total_return = stock_rets[-1:].transpose()
table['Total Return'] = 100 * total_return
table

	Returns	Risk	Sharpe Ratio	Max Returns	Min Returns	Median Returns	Total Return
ABBV	0.000553	0.020498	-0.460876	0.087174	-0.162524	0.001478	-2.229985
BUD	-0.001157	0.023911	-0.466629	0.108361	-0.160081	-0.000360	-6.103648
MO	-0.000263	0.019894	-0.515883	0.100220	-0.100296	0.001163	-2.255825
TAP	-0.000762	0.022924	-0.469473	0.108333	-0.111761	0.000220	-3.871481
SMG	0.001381	0.025713	-0.335198	0.146244	-0.182526	0.001494	-1.781241
CGC	0.000400	0.050582	-0.189792	0.304224	-0.200276	-0.003133	-3.074250
GWPH	0.000103	0.032086	-0.308445	0.150662	-0.170612	-0.001091	-2.656000
CRON	0.001340	0.054711	-0.158285	0.217225	-0.284144	-0.004667	-2.297092
ACB	-0.001118	0.070286	-0.158187	0.686747	-0.188889	-0.006305	0.662740
TLRY	0.001221	0.083543	-0.105087	0.567460	-0.322689	-0.005014	-4.789729
CRBP	0.001782	0.046494	-0.176764	0.536232	-0.159806	0.000000	-2.331283
CTST	-0.002506	0.061369	-0.203778	0.408889	-0.268139	0.000000	0.000000
NBEV	0.002995	0.090104	-0.077747	0.796339	-0.356098	-0.006292	-7.407408
TRTC	-0.002544	0.081239	-0.154405	0.470588	-0.300000	0.000000	0.000000
CANN	-0.002204	0.066023	-0.184838	0.461404	-0.370235	-0.011137	-3.948599
MJ	-0.000878	0.029951	-0.363179	0.100909	-0.125220	-0.001668	-2.876109

table['Average Return Days'] = (1 + total_return)**(1 / days) - 1
table

	Returns	Risk	Sharpe Ratio	Max Returns	Min Returns	Median Returns	Total Return	Average Return Days
ABBV	0.000553	0.020498	-0.460876	0.087174	-0.162524	0.001478	-2.229985	-0.000025
BUD	-0.001157	0.023911	-0.466629	0.108361	-0.160081	-0.000360	-6.103648	-0.000070
MO	-0.000263	0.019894	-0.515883	0.100220	-0.100296	0.001163	-2.255825	-0.000025
TAP	-0.000762	0.022924	-0.469473	0.108333	-0.111761	0.000220	-3.871481	-0.000044
SMG	0.001381	0.025713	-0.335198	0.146244	-0.182526	0.001494	-1.781241	-0.000020
CGC	0.000400	0.050582	-0.189792	0.304224	-0.200276	-0.003133	-3.074250	-0.000035
GWPH	0.000103	0.032086	-0.308445	0.150662	-0.170612	-0.001091	-2.656000	-0.000030
CRON	0.001340	0.054711	-0.158285	0.217225	-0.284144	-0.004667	-2.297092	-0.000026
ACB	-0.001118	0.070286	-0.158187	0.686747	-0.188889	-0.006305	0.662740	0.000007
TLRY	0.001221	0.083543	-0.105087	0.567460	-0.322689	-0.005014	-4.789729	-0.000054
CRBP	0.001782	0.046494	-0.176764	0.536232	-0.159806	0.000000	-2.331283	-0.000026
CTST	-0.002506	0.061369	-0.203778	0.408889	-0.268139	0.000000	0.000000	0.000000
NBEV	0.002995	0.090104	-0.077747	0.796339	-0.356098	-0.006292	-7.407408	-0.000085
TRTC	-0.002544	0.081239	-0.154405	0.470588	-0.300000	0.000000	0.000000	0.000000
CANN	-0.002204	0.066023	-0.184838	0.461404	-0.370235	-0.011137	-3.948599	-0.000045
MJ	-0.000878	0.029951	-0.363179	0.100909	-0.125220	-0.001668	-2.876109	-0.000032

initial_value = df.iloc[0]
ending_value = df.iloc[-1]
table['CAGR'] = ((ending_value / initial_value) ** (252.0 / days)) -1
table

	Returns	Risk	Sharpe Ratio	Max Returns	Min Returns	Median Returns	Total Return	Average Return Days	CAGR
ABBV	0.000553	0.020498	-0.460876	0.087174	-0.162524	0.001478	-2.229985	-0.000025	0.028927
BUD	-0.001157	0.023911	-0.466629	0.108361	-0.160081	-0.000360	-6.103648	-0.000070	-0.191453
MO	-0.000263	0.019894	-0.515883	0.100220	-0.100296	0.001163	-2.255825	-0.000025	-0.110096
TAP	-0.000762	0.022924	-0.469473	0.108333	-0.111761	0.000220	-3.871481	-0.000044	-0.186360
SMG	0.001381	0.025713	-0.335198	0.146244	-0.182526	0.001494	-1.781241	-0.000020	0.068500
CGC	0.000400	0.050582	-0.189792	0.304224	-0.200276	-0.003133	-3.074250	-0.000035	-0.114805
GWPH	0.000103	0.032086	-0.308445	0.150662	-0.170612	-0.001091	-2.656000	-0.000030	-0.026709
CRON	0.001340	0.054711	-0.158285	0.217225	-0.284144	-0.004667	-2.297092	-0.000026	NaN
ACB	-0.001118	0.070286	-0.158187	0.686747	-0.188889	-0.006305	0.662740	0.000007	-0.446040
TLRY	0.001221	0.083543	-0.105087	0.567460	-0.322689	-0.005014	-4.789729	-0.000054	NaN
CRBP	0.001782	0.046494	-0.176764	0.536232	-0.159806	0.000000	-2.331283	-0.000026	0.000350
CTST	-0.002506	0.061369	-0.203778	0.408889	-0.268139	0.000000	0.000000	0.000000	NaN
NBEV	0.002995	0.090104	-0.077747	0.796339	-0.356098	-0.006292	-7.407408	-0.000085	-0.103526
TRTC	-0.002544	0.081239	-0.154405	0.470588	-0.300000	0.000000	0.000000	0.000000	-0.684544
CANN	-0.002204	0.066023	-0.184838	0.461404	-0.370235	-0.011137	-3.948599	-0.000045	-0.565101
MJ	-0.000878	0.029951	-0.363179	0.100909	-0.125220	-0.001668	-2.876109	-0.000032	-0.222094

table.sort_values(by='Average Return Days')

	Returns	Risk	Sharpe Ratio	Max Returns	Min Returns	Median Returns	Total Return	Average Return Days	CAGR
NBEV	0.002995	0.090104	-0.077747	0.796339	-0.356098	-0.006292	-7.407408	-0.000085	-0.103526
BUD	-0.001157	0.023911	-0.466629	0.108361	-0.160081	-0.000360	-6.103648	-0.000070	-0.191453
TLRY	0.001221	0.083543	-0.105087	0.567460	-0.322689	-0.005014	-4.789729	-0.000054	NaN
CANN	-0.002204	0.066023	-0.184838	0.461404	-0.370235	-0.011137	-3.948599	-0.000045	-0.565101
TAP	-0.000762	0.022924	-0.469473	0.108333	-0.111761	0.000220	-3.871481	-0.000044	-0.186360
CGC	0.000400	0.050582	-0.189792	0.304224	-0.200276	-0.003133	-3.074250	-0.000035	-0.114805
MJ	-0.000878	0.029951	-0.363179	0.100909	-0.125220	-0.001668	-2.876109	-0.000032	-0.222094
GWPH	0.000103	0.032086	-0.308445	0.150662	-0.170612	-0.001091	-2.656000	-0.000030	-0.026709
CRBP	0.001782	0.046494	-0.176764	0.536232	-0.159806	0.000000	-2.331283	-0.000026	0.000350
CRON	0.001340	0.054711	-0.158285	0.217225	-0.284144	-0.004667	-2.297092	-0.000026	NaN
MO	-0.000263	0.019894	-0.515883	0.100220	-0.100296	0.001163	-2.255825	-0.000025	-0.110096
ABBV	0.000553	0.020498	-0.460876	0.087174	-0.162524	0.001478	-2.229985	-0.000025	0.028927
SMG	0.001381	0.025713	-0.335198	0.146244	-0.182526	0.001494	-1.781241	-0.000020	0.068500
CTST	-0.002506	0.061369	-0.203778	0.408889	-0.268139	0.000000	0.000000	0.000000	NaN
TRTC	-0.002544	0.081239	-0.154405	0.470588	-0.300000	0.000000	0.000000	0.000000	-0.684544
ACB	-0.001118	0.070286	-0.158187	0.686747	-0.188889	-0.006305	0.662740	0.000007	-0.446040